An image-based decision system processes and extracts information from an image or multiple images to make decisions such as the presence of objects of interest, disease, defects; or the acceptance of measurement parameters such as dimensions, intensity, structures, etc. Image-based decision systems have broad applications such as machine vision, non-contact gauging, inspection, robot guidance, medical imaging, biometrics, vision-guided information appliance, etc.
Many image-based decision functions involve the detection of defects or gauging of dimensions from man-made manufacturing components, parts or systems. Filtering, thresholding, template matching, golden template comparison and caliper based edge detection are the primary prior art approaches for performing simple machine vision inspection and measurement tasks (Silver, B, “Geometric Pattern Matching for General-Purpose Inspection in Industrial Machine Vision”, Intelligent Vision '99 Conference—Jun. 28–29, 1999.).
There is often a teaching phase and an application phase for an image-based decision system. In the prior art approach, template region(s)or a golden template is selected by a human and stored in the system in the teaching phase. In addition, edge detection calipers are specified at image regions of interest for edge detection through multiple one-dimensional projection and simple differentiation. In the application phase, template search is applied to locate the template region(s) in the input image. The located template locations are used to establish a reference coordinate system and/or for deriving points and structures for measurements. Edges are detected from each caliper region and/or the golden template is subtracted from the normalized input image for defect detection or dimensional measurements (Hanks, J, “Basic Functions Ease Entry Into Machine Vision”, Test & Measurement World, Mar. 1, 2000 http://www.e-insite.net/tmworld/index.asp?layout=article&articleid=CA187377&pubdate=3/1/2000, Titus, J, “Software makes machine vision easier”, Test & Measurement World, Oct. 15, 2001 http://www.e-insite.net/tmworld/index.asp?layout=article&articleid=CA177596&pubdate=10/15/2001)
In objects with many components, there is no systematic way of separating variation effects of each component in the prior art approaches. Each component of an object is detected by a local based processing method that detects local features without taking into account the features detected from other components of the object. This isolated local detection result is easily degraded by noise and variations. This leads to inaccurate measurements, inconsistent results, missed defects or false alarms. Therefore, in order to accommodate components with large variations, defects in components with small variations may be missed. Conversely, in order to detect defects in components with small variations, false alarms may be detected from components with large variations. Furthermore, in objects with many components, there is no systematic way of separating affects of each component in the prior art approaches. Therefore, a defective component in an object may hinder the ability of the inspection system to properly inspect the other components of the object.
Prior art approaches do not have a systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency. For example, a line component 1 is designed to be parallel to a line component 2. When a 10 degree rotation is detected in line component 1, line component 2 is assumed to be rotated by 10 degrees as well. If the measured rotation angle of line component 2 does not match that of line component 1, a method of conflict resolution and estimate refinement should be provided. This is not included in the prior art framework. A special application dependent ad-hoc approach is sometimes used in the prior art if the structure linkage is desirable.
Increasing quality and precision requirements in advanced manufacturing demands that quality control procedures be implemented at every stage of the manufacturing process. This requires advanced inspection applications to be deployed on the factory floor by users who have little or no knowledge of image processing/pattern recognition/machine vision technology. Simple prior art algorithms cannot properly address these requirements. There are growing demands for new solutions for image-based decisions. The new solution should allow use of application knowledge to optimize the detection results.
A multilevel Chain-And-Tree (CAT) model was disclosed for image based decision in Lee, S., Huang, C., entitled “A Multilevel Chain-And-Tree Model for Image-based Decisions”, (“CATID”) U.S. patent application Ser. No. 10/104,669, filed Mar. 22, 2002, which is incorporated in its entirety herein. It provides a framework to facilitate highly effective analysis and measurement for advanced image based decision.
Since components occur as parts of an object, the context (i.e. relational structure) in which the component appears can be used to reduce noise and variation affects. In the CATID method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, measurement accuracy, defect detection and consistency. Use of constraints achieves robust results for image-based decisions.
A major advantage of the CAT model is the ability to link components through pair-wise relations. The component linking allows the refinement of local detection of a CAT node component using the detection results of all other CAT nodes. The pair-wise serial component relations allow very efficient optimization procedures that optimizes all detection results using all relations. Therefore, the CAT model detection results and defect detection from all components are considered before rendering the final detection results of a component even though the relations from one component to most of the other components are indirect.